Orthogonal Projections To Latent Structures Discriminant Analysis Research Articles

Associations of Plasma and Fecal Metabolites with Body Mass Index and Body Fat Distribution in Children.

Childhood obesity continues to be a critical public health concern with far-reaching implications for the well-being. This study aimed to investigate the association between metabolites in plasma and feces and indicators including body mass index (BMI), BMI for age Z score (BMIZ), and body fat distribution among children aged 6-9 years in China. This cross-sectional study enrolled 424 healthy children, including 186 girls and 238 boys. Dual-energy X-ray absorptiometry (DXA) was used to determine the body fat content and regional fat distribution. Plasma and fecal metabolites were analyzed using targeted metabolomic technologies. A total of 200 plasma metabolites and 212 fecal metabolites were accurately quantified via ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). By using Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and random forest model, we discovered that 9 plasma metabolites and 11 fecal metabolites were associated with different weight statuses. After adjusting for potential covariates and false discovery rate (FDR) correction, multiple linear regression analyses revealed that plasma metabolites (fumaric acid, glycine, l-glutamine, methylmalonic acid, and succinic acid) and fecal metabolites (protocatechuic acid) were negatively associated (β: -1.373--0.016, pFDR: <0.001-0.031; β: -1.008--0.071, pFDR: 0.005-0.033), while plasma metabolites (isovaleric acid, isovalerylcarnitine, l-glutamic acid, and pyroglutamic acid) and fecal metabolites (3-aminoisobutanoic acid, butyric acid, N-acetylneuraminic acid, octanoylcarnitine, oleoylcarnitine, palmitoylcarnitine, stearoylcarnitine, taurochenodesoxycholic acid, and taurodeoxycholic acid) exhibited positive associations with BMI, BMIZ, and body fat distribution (β: 0.023-2.396, pFDR: <0.001; β: 0.014-1.736, pFDR: <0.001-0.049). Plasma and fecal metabolites such as glutamine may serve as a potential therapeutic target for the development of obesity.

Revealing the 1H-NMR Profiling of Six Edible Mushrooms Consumed in the Northeastern Highlands of Puebla, Mexico.

The 1H-NMR metabolomics profiling of six edible mushrooms consumed in the northeastern highlands of Puebla, Mexico is presented. These fungi were morpho- and molecularly identified as Infundibulicybe squamulosa, Amanita jacksonii, Lepista nuda, Russula delica, Russula brevipes, and Lactarius indigo. The chemical profiling confirmed the presence of eight essential amino acids and their derivatives, six organic acids, six nucleosides, low amounts of reducing sugars, and valuable nutraceuticals such as betaine, carnitine, glycero-3-phosphocholine and O-acetylcarnitine which were differentially determined and quantified in the six mushrooms by qNMR. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) generated four different groups. Two of these groups were constituted by fungal species with phylogenic relationships whereas non-phylogenetic related species were separated from each other. The potential use of 1H-NMR metabolomics and chemometrics to group macromycetes and determine the nutritional and nutraceutical potential of these local foods is demonstrated.

Distinct Changes in Metabolic Profile and Sensory Quality with Different Varieties of Chrysanthemum (Juhua) Tea Measured by LC-MS-Based Untargeted Metabolomics and Electronic Tongue.

Chrysanthemum tea, a typical health tea with the same origin as medicine and food, is famous for its unique health benefits and flavor. The taste and sensory quality of chrysanthemum (Juhua) tea are mainly determined by secondary metabolites. Therefore, the present research adopted untargeted metabolomics combined with an electronic tongue system to analyze the correlation between the metabolite profiles and taste characteristics of different varieties of chrysanthemum tea. The results of sensory evaluation showed that there were significant differences in the sensory qualities of five different varieties of chrysanthemum tea, especially bitterness and astringency. The results of principal component analysis (PCA) indicated that there were significant metabolic differences among the five chrysanthemum teas. A total of 1775 metabolites were identified by using untargeted metabolomics based on UPLC-Q-TOF/MS analysis. According to the variable importance in projection (VIP) values of the orthogonal projections to latent structures discriminant analysis (OPLS-DA), 143 VIP metabolites were found to be responsible for metabolic changes between Huangju and Jinsi Huangju tea; among them, 13 metabolites were identified as the key metabolites of the differences in sensory quality between them. Kaempferol, luteolin, genistein, and some quinic acid derivatives were correlated with the "astringency" attributes. In contrast, l-(-)-3 phenyllactic acid and L-malic acid were found to be responsible for the "bitterness" and "umami" attributes in chrysanthemum tea. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that the flavonoid and flavonol biosynthesis pathways had important effects on the sensory quality of chrysanthemum tea. These findings provide the theoretical basis for understanding the characteristic metabolites that contribute to the distinctive sensory qualities of chrysanthemum tea.

Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined With Deep Learning for Classification of Idiopathic Recurrent Spontaneous Miscarriage (IRSM)

Idiopathic recurrent spontaneous miscarriage (IRSM) is characterized by the repeated losses of two or more clinically detected pregnancies before 24 week of gestation with the cause remaining unknown. We aim to ascertain whether attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy with deep learning (DL) can accurately distinguish IRSM from controls. Endometrial tissue (internal lining of the uterine cavity) was collected from 20 women with IRSM and 20 controls during window of implantation. ATR-FTIR spectroscopy was used to acquire spectra (400–4000 cm−1) from three random locations across each specimen and averaged. Multivariate analysis was used to assess group differentiation. Furthermore, DL classifiers were implemented to predict IRSM. Hierarchical cluster analysis (HCA) demonstrated distinct clustering of the two groups. The principal component analysis (PCA) loading plot indicated contributions from amide I, II and III bands, symmetric methyl (CH3) bending of proteins, lipids, nucleic acids, asymmetric stretching of CH2 due to lipids, collagen, and glucose in discriminating between IRSM and controls. Supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA) exhibited optimized discrimination between the groups. Convolutional neural network (CNN) and variational autoencoder-artificial neural network (VAE-ANN) classifiers exhibited good performance and showed classification accuracy of 94% and 94% and F1 scores (mean of recall and precision) of 95% and 93%, respectively. The optimal area under the receiver operating characteristic curve (AUC) value of both CNN and VAE-ANN was 0.94. ATR-FTIR spectroscopy combined with DL approach is promising for understanding complex disease pathophysiology and enabling early prediction.

Geographical origin identification of winter jujube (Ziziphus jujuba ‘Dongzao’) by using multi-element fingerprinting with chemometrics

Winter jujube (Ziziphus jujuba ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.

The Shelf Life of Milk-A Novel Concept for the Identification of Marker Peptides Using Multivariate Analysis.

The quality of food is influenced by several factors during production and storage. When using marker compounds, different steps in the production chain, as well as during storage, can be monitored. This might enable an optimum prediction of food's shelf life and avoid food waste. Especially, proteoforms and peptides thereof can serve as indicators for exogenous influences. The development of a proteomics-based workflow for detecting and identifying differences in the proteome is complex and time-consuming. The aim of the study was to develop a fast and universal workflow with ultra-high temperature (UHT) milk as a proteinaceous model food with expectable changes in protein/peptide composition. To find an optimum shelf life without sticking to a theoretically fixed best-before date, new evaluation and analytical methods are needed. Consequently, a modeling approach was used to monitor the shelf life of the milk after it was treated thermally and stored. The different peptide profiles determined with high-resolution mass spectrometry (HRMS) showed a significant difference depending on the preparation method of the samples. Potential marker peptides were determined using orthogonal projections to latent structures discriminant analysis (OPLSDA) and principal component analysis (PCA) following a typical proteomics protocol with tryptic hydrolysis. An additional Python-based algorithm enabled the identification of eight potential tryptic marker peptides (with mass spectrometric structural indications m/z 885.4843, m/z 639.3500, m/z 635.8622, m/z 634.3570, m/z 412.7191, m/z 623.2967, m/z 880.4767, and m/z 692.4041), indicating the effect of the heat treatment. The developed workflow is flexible and can be easily adapted to different research questions in the field of peptide analysis. In particular, the process of feature identification can be carried out with significantly less effort than with conventional methods.

Thrombosis Rates and Genetic Thrombophilia Risk Among Patients With Advanced Germ Cell Tumors Treated With Chemotherapy

IntroductionMen with advanced germ cell tumors (GCT) treated with chemotherapy are at high risk of venous thromboembolism (VTE). Predictors of VTE may identify patients who would benefit from prophylactic anticoagulation. Patients and MethodsMen with advanced GCT (Stage IS, II, III) treated with chemotherapy were identified at 2 centers. High genomic risk was defined from a 5 single nucleotide polymorphism (SNP) germline panel. Logistic regression was used to evaluate the impact of genomic risk on VTE within 6 months of chemotherapy initiation. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) was used to build models to predict VTE based on clinical variables and an 86 SNP panel. ResultsThis 123-patient cohort experienced a VTE rate of 26% with an incidence of high genomic risk of 21%. Men with high genomic risk did not have a significantly higher VTE rate (31%, 8/26) than men with low genomic risk (25%, 24/97), unadjusted OR 1.4 (95% CI 0.5-3.5, P = .54). Incorporation of clinical variables (Khorana score, N3 status and elevated LDH) resulted in adjusted OR 2.1 (95% CI 0.7-6.5, P = .18). A combined model using clinical variables and 86 SNPs performed similarly (AUC 0.77) compared to clinical variables alone (AUC 0.72). ConclusionsA previously established 5-SNP panel was not associated with VTE among patients with GCT receiving chemotherapy. However, multivariable models based on clinical variables alone warrant further validation to inform prophylactic anticoagulation strategies.

Investigation into the role of phenolic compounds in the protection of citrus against Phyllosticta citricarpa

IntroductionThe use of chemical fungicides to combat disease has made a substantial contribution to food quality and security. Nonetheless, their applications have been limited due to environmental and health concerns, unaffordability, and the fact that pathogens have acquired resistance to some of these fungicides. Alternative eco-friendly and safe control methods should be explored. The current study investigated the influence of citrus rind phenolic compounds against Phyllosticta citricarpa infection by metabolic profiling of two citrus cultivars with varying degrees of susceptibility to infection.MethodsChromatographic data obtained by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC) was subjected to multivariate data analysis to identify biomarkers associated with the tolerant cultivar. The identified biomarkers were tested in vitro against P. citricarpa.ResultsSeville oranges, a tolerant cultivar, displayed higher levels of phenolic content and lower total sugar content, that are both associated with lower susceptibility to citrus black spot infection. The generated Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA) models gave an overview of the data set and identified components that may be responsible for the differences in susceptibility between the two cultivars. Candidate biomarkers associated with tolerance were identified as naringin, neoeriocitrin, bruteiridin, melitidin, and lucenin-2.ConclusionNaringin, a major candidate biomarker was able to inhibit the growth of the pathogen at 10 000 ppm.

Characteristic flavor compounds in Guizhou green tea and the environmental factors influencing their formation: Investigation using stable isotopes, electronic nose, and headspace-gas chromatography ion migration spectrometry

The Guizhou province is a significant contributor to the production of green tea within China. The present study aims to examine the characteristic flavor compounds found in Guizhou green tea and explore the environmental factors that contribute to the development of these unique flavor compounds. Accordingly, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and electronic nose were employed in conjunction with odor activity value (OAV) analysis to develop an orthogonal projections to latent structures discriminant analysis (OPLS-DA) model that was capable of discerning between the three distinct habitats (QE, QM, QW) of Guizhou green tea based on their flavor constituents. The findings show that the primary constituents contributing to the flavor profile of Guizhou green tea consisted of 3-methyl-2-butenal, butanal, pentanal, linalool, 6-methyl-5-hepten-2-one, dimethyl disulfide, and dimethyl sulfide. Subsequently, the stable isotope technique was employed to examine the growth environmental factors of Guizhou green tea, based on the identification of the flavoring compounds that differentiate green tea from various habitats within the Guizhou province. Accordingly, temperature and sunshine were found to have the greatest impact on the flavor compounds present in Guizhou green tea.

A multiplex metabolomic approach for quality control of Spirulina supplement and its allied microalgae (Amphora & Chlorella) assisted by chemometrics and molecular networking

Microalgae species are of economic importance regarded as “green gold” being rich in bioactive compounds. Spirulina and Chlorella are the most popular microalgal species and are marketed as healthy food supplements. At the same time, Amphora holds potential as a source of healthy lipids and essential fatty acids. Yet, there are considerable variations in their reported chemical composition, and less is known about their compositional differences. A multiplexed metabolomic approach was adopted for the quality control (QC) of Spirulina supplements and to compare its constitutive metabolome to Chlorella and Amphora. The adopted protocol comprised gas chromatography-mass spectrometry (GC–MS), ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS), and ultraviolet–visible spectrophotometry (UV/Vis) for mapping their primary and secondary metabolome. Interestingly, UPLC-HRMS/MS analysis delineated the abundance of fatty acids in Amphora versus glycolipids enrichment in Spirulina, and porphyrins were the main pigments identified in Spirulina, with scarce occurrence in Chlorella. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) analysis of GC–MS data set revealed palmitic acid, 3-mannobiose, and glyceryl-glycoside as being most enriched in Spirulina, versus sucrose and leucine in Chlorella and Amphora, respectively. Despite being of low discriminatory potential, UV/Vis OPLS-DA modeling showed that Spirulina was distinguished with the UV absorbances of carotenoids and chlorophyll pigments, as indicated by its OPLS-DA derived S-plot. Our study provides a QC approach for the analysis of the microalgal species and poses alternative spectral and compositional markers for their discrimination.

Oxidative Stability and Pasting Properties of High-Moisture Japonica Brown Rice following Different Storage Temperatures and Its Cooked Brown Rice Flavor

The study proposed to investigate the impacts of storage temperatures (15, 20, 25 °C) on the oxidative stability (peroxide value, carbonyl value, malondialdehyde content) and sensory attributes (pasting properties, cooked brown rice flavor) of high-moisture japonica brown rice. According to the findings, the peroxide value, the carbonyl value, and the malondialdehyde content of high-moisture japonica brown rice stored at a temperature of 15 °C exhibited consistently low levels, and the pasting properties were favorable. In addition, 22 out of 51 flavor volatiles were screened as key differential volatile flavor compounds in cooked brown rice via a combination of ANOVA and orthogonal projections to latent structures-discriminant analysis (OPLS-DA). Among them, 3-heptylacrolein had an aroma of fat and mushroom, and its contents were higher at 15 °C and 20 °C. These findings could serve as a valuable reference for storing high-moisture japonica brown rice under low temperature conditions as well as for investigating the flavor characteristics of cooked brown rice derived from this variety.

Reflection of Physical Descriptive Parameters as Functional Phenes on Olive Oil Cross-border Mapping

The medicinal and nutritional benefits of olive oil are reflected in its extensive incorporation in many cultures in food and pharmaceutical products. This study suggests the use of colour-based models, viscosity and refractive index as parametric attributes to experiment and track down the authenticity of the oil through image analysis and accurate determination of refractive index and viscosity of oils from different regions.The combination of RGB measurement and chemometric methods (PCA, PCA-class) of olive oil samples collected from distinctive areas (Tunisia, Libya and Egypt) enabled the differentiation of olive oil samples per region of cultivation and was found to be accurate, precise (R2X 0.997) and successful in detecting the accuracy of the labelled information (traceability determination, freshness, purity, date and storage conditions) and could be extrapolated to study the position of harvested areas. The results also revealed a perfect discrimination between the defined categories based on the origin and age after performing the Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA) on the visible spectra of the samples reaching 90% of correct classifications.

Does Keeping Cows for More Lactations Affect the Composition and Technological Properties of the Milk?

This study investigated differences in the raw milk composition and technological properties between cows with different numbers of lactations. In total, 12 commercial herds were visited within a period of 12 weeks. On each farm, milk samples from five young cows (lactations 1-2) and five older cows (lactation ≥ 3) were collected. For each farm, milk samples from the young cows and the older cows, respectively, were pooled. The pooled milk samples were analyzed for gross composition and technological properties. Using principal component analysis (PCA) to assess the overall variation in milk quality attributes and the potential clustering of milk from young cows and older cows, respectively, an effect of breed, but no clear effect of lactation number, was observed. In contrast, one-way ANOVA showed higher plasmin activity (p = 0.002) in pooled milk from the older cows, whereas plasminogen-derived activity (p = 0.001) and total proteolysis (p = 0.029) were higher in milk from the young cows. Likewise, orthogonal projections to latent structure discriminant analysis (OPLS-DA) showed higher plasmin activity in milk from older cows, whereas younger cows had higher plasminogen-related activity and higher total proteolysis. To conclude, except for plasmin and plasminogen-related activities, there were no major differences in the composition and technological properties between milk from older cows and young cows.

Metabolomic profiling of Medicago sativa-derived fungal endophytes and evaluation of their biological activities.

This study aimed to discover the potential of Medicago sativa-derived fungal endophytes as a prospective source of bioactive metabolites. In the present study, three different strains of fungal endophyte Aspergillus terreus were isolated from leaves L, roots T and stems St of Medicago sativa to explore their biological and chemical diversity. These isolated fungi were exposed to different fermentation conditions by adding various chemical elicitors to their solid fermentation media. According to LC-HRESIMS-based metabolomics and multivariate analysis, each chemical treatment had a different effect on the chemical profiles of the fungi. Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) proposed several compounds with anticancer action against MCF-7 (a human breast cancer cell line) and MDA-MB-231 (a human epithelial breast cancer cell line).

Breath Analysis for Lung Cancer Early Detection-A Clinical Study.

This clinical study presents a comprehensive investigation into the utility of breath analysis as a non-invasive method for the early detection of lung cancer. The study enrolled 14 lung cancer patients, 14 non-lung cancer controls with diverse medical conditions, and 3 tuberculosis (TB) patients for biomarker discovery. Matching criteria including age, gender, smoking history, and comorbidities were strictly followed to ensure reliable comparisons. A systematic breath sampling protocol utilizing a BIO-VOC sampler was employed, followed by VOC analysis using Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). The resulting VOC profiles were subjected to stringent statistical analysis, including Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA), Kruskal-Wallis test, and Receiver Operating Characteristic (ROC) analysis. Notably, 13 VOCs exhibited statistically significant differences between lung cancer patients and controls. The combination of eight VOCs (hexanal, heptanal, octanal, benzaldehyde, undecane, phenylacetaldehyde, decanal, and benzoic acid) demonstrated substantial discriminatory power with an area under the curve (AUC) of 0.85, a sensitivity of 82%, and a specificity of 76% in the discovery set. Validation in an independent cohort yielded an AUC of 0.78, a sensitivity of 78%, and a specificity of 64%. Further analysis revealed that elevated aldehyde levels in lung cancer patients' breath could be attributed to overactivated Alcohol Dehydrogenase (ADH) pathways in cancerous tissues. Addressing methodological challenges, this study employed a matching of physiological and pathological confounders, controlled room air samples, and standardized breath sampling techniques. Despite the limitations, this study's findings emphasize the potential of breath analysis as a diagnostic tool for lung cancer and suggest its utility in differentiating tuberculosis from lung cancer. However, further research and validation are warranted for the translation of these findings into clinical practice.

Application of 1H NMR metabolomics analysis to sparkling wines aged with different types of sugar added to the liqueur de dosage

Over the past decade, 1H NMR metabolomic studies have become increasingly popular in wine research since they can provide a chemical fingerprint with information regarding authenticity, geographical origin, vintage, and wine composition. Comparatively few 1H NMR-based studies have evaluated sparkling wines. In the present study, quantitative 1H NMR metabolomics was applied to assess the composition of finished traditional method (bottle-fermented) sparkling wines aged for 18-months in cellar conditions (14 °C, 70% relative humidity) following liqueur d′expedition (dosage) additions with six sugar types (glucose, fructose, sucrose derived from sugar beets and sugar cane, maltose, and MCR Sucraisin®) and a zero-dosage control. No systematic trends were observed when comparing the metabolite profile across sugar types, nor between the zero-dosage control and treatments with sugar additions. Multivariate statistical approaches using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) did not effectively discriminate between dosage treatments nor wine age, while orthogonal projections to latent structures discriminant analysis (OPLS-DA) successfully discriminated between wines by age and was validated through permutation tests. This study presents the first application of 1H NMR metabolomics coupled with multivariate analysis to monitor the aging of sparkling wines.

Migration of contaminants from printed masks for children to saliva simulant using liquid chromatography coupled to ion mobility-time of flight-mass spectrometry and gas chromatography-mass spectrometry

The COVID-19 pandemic has led to children using polymeric FFP2 and polymeric surgical masks on a daily basis. Children often bite and suck on such masks as they wear them closed to their mouths. In this work, the migration of contaminants from printed and unprinted children`s masks to a saliva simulant has been studied. Liquid chromatography coupled to ion-mobility quadrupole time-of-flight mass spectrometry has been used for the detection and identification of non-volatile migrants. An orthogonal projection to latent structures – discriminant analysis (OPLS-DA) was applied to compare the data from the printed masks against the data from the unprinted ones. Headspace solid phase microextraction coupled to gas chromatography mass spectrometry was used to assess the migration of volatile compounds. Thirteen compounds were found in the masks with concentrations ranging from 5 ng/g to 254 ng/g. Toluene, chlorobenzene, irganox 1076 and 2-(2-butoxyethoxy)ethyl acetate were all found to migrate from the masks studied. Moreover, differences between the migrants from printed and unprinted FFP2 masks were found. Octocrylene, 4-(dimethylamine)benzoate, methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate and tris(3-methylphenyl)phosphate were found to migrate only from printed masks. Toluene that migrated from all the masks studied and tris(3-methylphenyl)phosphate, that migrated only from printed masks, have been listed as hazardous priority substances.

Chemometric-enhanced metabolic profiling of different plant parts belonging to two Bougainvillea species using UPLC-MS/MS spectrometry: Correlation to in vitro anti-inflammatory activity

Bougainvillea is considered as an important source of environment-friendly safe pigmenting agents, namely anthocyanins that are widely used in food, textile, cosmetic, and pharmaceutical industries. This study aimed to investigate the resemblance and variation in the chemical profiles of B. spectabilis Willd and B. glabra Choisy flowers and leaves extracts utilizing UPLC-MS/MS analysis in combination with chemometric tools. Forty-four compounds were tentatively annotated in the tested Bougainvillea extracts. Flavonoids, anthocyanins, mono, di, and triterpenes in addition to phenolic acids were the major classes of the annotated compounds. B. spectabilis flowers samples showed significant variations in their chemical profiles compared to the other tested extracts. The downregulating effect of the tested extracts on the expression of four pro-inflammatory markers (TNF-α, IL-6, IL-1β, and INF-γ) genes was assessed using real-time-PCR technique. All tested extracts abolished pro-inflammatory genes upregulation to levels comparable to those exhibited by piroxicam, on top of which were B. spectabilis leaves that exhibited the strongest anti-inflammatory activity due to their enrichment of bioactive metabolites that significantly influenced the expression of the proinflammatory biomarkers. Orthogonal projection to latent structure-discriminant analysis (OPLS) and coefficient plots unveiled the biomarkers contributing to the anti-inflammatory efficacy including Caffeic acid hexoside, p-hydroxybenzoic acid, sinapinic acid, quercetin 3-O-(4-caffeoylrhamnosyl-rhamnosyl-hexoside), rutin, neoeriocitrin, luteolin robinobioside, quercetin glucuronide (miquelianin) that seemed to be profoundly dominant in B.spectabilis leaves samples. This study showed that Bougainvillea plants can serve as important sources of valuable anti-inflammatory compounds besides their pronounced industrial applications.

Pseudomonassin, a New Bioactive Ribosomally Synthesised and Post-Translationally Modified Peptide from Pseudomonas sp. SST3.

Genome mining and metabolomics have become valuable tools in natural products research to evaluate and identify potential new chemistry from bacteria. In the search for new compounds from the deep-sea organism, Pseudomonas sp. SST3, from the South Shetland Trough, Antarctica, a co-cultivation with a second deep-sea Pseudomonas zhaodongensis SST2, was undertaken to isolate pseudomonassin, a ribosomally synthesised and post-translationally modified peptide (RiPP) that belongs to a class of RiPP called lasso peptides. Pseudomonassin was identified using a genome-mining approach and isolated by means of mass spectrometric guided isolation. Extensive metabolomics analysis of the co-cultivation of Pseudomonas sp. SST3 and P. zhaodongensis SST2, Pseudomonas sp. SST3 and Escherichia coli, and P. zhaodongensis SST2 and E. coli were performed using principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA), which revealed potential new metabolites in the outlier regions of the co-cultivation, with other metabolites identified previously from other species of Pseudomonas. The sequence of pseudomonassin was completely deduced using high collision dissociation tandem mass spectrometry (HCD-MS/MS). Preliminary studies on its activity against the pathogenic P. aeruginosa and its biofilm formation have been assessed and produced a minimum inhibitory concentration (MIC) of 63 μg/mL and 28 μg/mL, respectively.

Insight into the effect of cultivar and altitude on the identification of EnshiYulu tea grade in untargeted metabolomics analysis

The accurate identification of tea grade is crucial to the quality control of tea. However, existing methods lack sufficient generalization ability in identifying tea grades due to the effect of temporal and spatial factors. In this study, we analyzed the effect of cultivar and altitude on EnshiYulu (ESYL) tea grades and established a robust model to evaluate their quality. Principal component analysis (PCA) revealed that differences in variety and elevation can mask grade differences. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) was used for grade identification of samples from different altitudes. For ESYL tea samples above and below 800 m altitude, 75 and 35 grade differentiated metabolites were discovered, with 14 common differentiated metabolites. Based on reconstructed OPLS-DA models, the grades of multi-altitude sources ESYL were discriminated with a rate > 85%. These results demonstrate the potential of a grade discrimination model based on common differential metabolites, which exhibits generalization ability.